{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sdm as sdmlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run(n, H, r, d, steps=100):\n",
    "    \"\"\"\n",
    "    n = bits\n",
    "    H = number of hardlocations\n",
    "    r = radius\n",
    "    d = distance between bitstrings\n",
    "    \"\"\"\n",
    "    as1 = sdmlib.AddressSpace.init_random(n, H)\n",
    "    v = []\n",
    "    for _ in range(steps):\n",
    "        bs1 = sdmlib.Bitstring.init_random(n)\n",
    "        bs2 = bs1.copy()\n",
    "        bs2.flip_random_bits(d)\n",
    "        selected1 = as1.scan_thread2(bs1, r)\n",
    "        selected2 = as1.scan_thread2(bs2, r)\n",
    "        x = len(set(selected1) & set(selected2))\n",
    "        v.append(x)\n",
    "    return v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = run(1000, 1000000, 451, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('mu', 1070.25)\n",
      "('var', 1065.5475)\n"
     ]
    }
   ],
   "source": [
    "mu = 1.0*sum(v)/len(v)\n",
    "var = sum((x-mu)**2 for x in v)/len(v)\n",
    "print('mu', mu)\n",
    "print('var', var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11394b110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6), dpi=300)\n",
    "plt.hist(v, bins='fd');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
